Skip Navigation


Bioinformatics Advance Access first published online on August 25, 2007
This version published online on September 1, 2007

Bioinformatics, doi:10.1093/bioinformatics/btm412
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrowOA All Versions of this Article:
23/20/2700    most recent
btm412v2
btm412v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Ritchie, M. E.
Right arrow Articles by Smyth, G. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ritchie, M. E.
Right arrow Articles by Smyth, G. K.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A comparison of background correction methods for two-colour microarrays

Matthew E. Ritchie a, Jeremy Silver b, Alicia Oshlack b, Melissa Holmes c, Dileepa Diyagama d, Andrew Holloway d and Gordon K. Smyth b,*

aDepartment of Oncology, University of Cambridge, CRUK Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, United Kingdom. bBioinformatics Division, cImmunology Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3050, Australia, dThe Peter MacCallum Cancer Centre, St Andrews Place, East Melbourne, Victoria 3002, Australia

*To whom correspondence should be addressed. Gordon K. Smyth, E-mail: smyth{at}wehi.edu.au


   Abstract

Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments.

Results: Using data where some independent truth in gene expression is known, 8 different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates.Methods which stabilise the variances of the log-ratios along the intensity range perform the best.The normexp + offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp isapplicable to most types of two-colour microarray data.

Availability: The background correction methods compared in this paper are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. Supplementary Information is available from http://bioinf.wehi.edu.au/resources/webReferences.html.

Contact: smyth{at}wehi.edu.au

Associate Editor: Dr. Trey Ideker


Received on April 16, 2007; revised on July 20, 2007; accepted on August 9, 2007

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Nucleic Acids ResHome page
L.-H. Ding, Y. Xie, S. Park, G. Xiao, and M. D. Story
Enhanced identification and biological validation of differential gene expression via Illumina whole-genome expression arrays through the use of the model-based background correction methodology
Nucleic Acids Res., June 1, 2008; 36(10): e58 - e58.
[Abstract] [Full Text] [PDF]


Home page
Nucleic Acids ResHome page
S. M. Lin, P. Du, W. Huber, and W. A. Kibbe
Model-based variance-stabilizing transformation for Illumina microarray data
Nucleic Acids Res., February 2, 2008; 36(2): e11 - e11.
[Abstract] [Full Text] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.